Crackle and wheeze detection in lung sound signals using convolutional neural networks

• Type: Article in International Conference Proceedings Book
• Date: 2021

Authors

Faustino, P (Author) Other; Oliveira, J (Author) Other; Coimbra, M (Author) FCUP

Conference proceedings International

Title: 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2021, Mexico, November 1-5, 2021

Pages: 345-348

Indexing

ISI Web of Knowledge - 4 Citations

Other information

Authenticus ID: P-00V-ZNQ

DOI: 10.1109/embc46164.2021.9630391

Abstract (EN): Respiratory diseases are among the leading causes of death worldwide. Preventive measures are essential to avoid and increase the odds of a successful recovery. An important screening tool is pulmonary auscultation, an inexpensive, noninvasive and safe method to assess the mechanics and dynamics of the lungs. On the other hand, it is a difficult task for a human listener since some lung sound events have a spectrum of frequencies outside of the human hearing ability. Thus, computer assisted decision systems might play an important role in the detection of abnormal sounds, such as crackle or wheeze sounds. In this paper, we propose a novel system, which is not only able to detect abnormal lung sound events, but it is also able to classify them. Furthermore, our system was trained and tested using the publicly available ICBHI 2017 challenge dataset, and using the metrics proposed by the challenge, thus making our framework and results easily comparable. Using a Mel Spectrogram as an input feature for our convolutional neural network, our system achieved results in line with the current state of the art, an accuracy of 43 %, and a sensitivity of 51%.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4